# Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts

arXiv: https://arxiv.org/abs/2012.11788 · HTML: https://mlsanity.com/paper/2012.11788/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 30 of 36)

- [On the Adversarial Robustness of Causal Algorithmic Recourse](https://mlsanity.com/paper/2112.11313/) (2021)
- [Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse](https://mlsanity.com/paper/2203.06768/) (2022)
- [On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations](https://mlsanity.com/paper/2201.09051/) (2022)
- [On the Privacy Risks of Algorithmic Recourse](https://mlsanity.com/paper/2211.05427/) (2022)
- [Robust Counterfactual Explanations in Machine Learning: A Survey](https://mlsanity.com/paper/2402.01928/) (2024)
- [On the Trade-Off between Actionable Explanations and the Right to be Forgotten](https://mlsanity.com/paper/2208.14137/) (2022)
- [Improvement-Focused Causal Recourse (ICR)](https://mlsanity.com/paper/2210.15709/) (2022)
- [Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation](https://mlsanity.com/paper/2309.12545/) (2023)
- [Finding Regions of Counterfactual Explanations via Robust Optimization](https://mlsanity.com/paper/2301.11113/) (2023)
- [Robust Bayesian Recourse](https://mlsanity.com/paper/2206.10833/) (2022)
- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Generating robust counterfactual explanations](https://mlsanity.com/paper/2304.12943/) (2023)
- [Setting the Right Expectations: Algorithmic Recourse Over Time](https://mlsanity.com/paper/2309.06969/) (2023)
- [Distributionally Robust Recourse Action](https://mlsanity.com/paper/2302.11211/) (2023)
- [RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model](https://mlsanity.com/paper/2206.00700/) (2022)
- [Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten](https://mlsanity.com/paper/2302.04288/) (2023)
- [The Risks of Recourse in Binary Classification](https://mlsanity.com/paper/2306.00497/) (2023)
- [Performative Validity of Recourse Explanations](https://mlsanity.com/paper/2506.15366/) (2025)
- [Interval Abstractions for Robust Counterfactual Explanations](https://mlsanity.com/paper/2404.13736/) (2024)
- [From Search To Sampling: Generative Models For Robust Algorithmic Recourse](https://mlsanity.com/paper/2505.07351/) (2025)
- [Prediction without Preclusion: Recourse Verification with Reachable Sets](https://mlsanity.com/paper/2308.12820/) (2023)
- [The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity](https://mlsanity.com/paper/2407.13070/) (2024)
- [On Minimizing the Impact of Dataset Shifts on Actionable Explanations](https://mlsanity.com/paper/2306.06716/) (2023)
- [Reinforcement Learning for Durable Algorithmic Recourse](https://mlsanity.com/paper/2509.22102/) (2025)
- [Generally-Occurring Model Change for Robust Counterfactual Explanations](https://mlsanity.com/paper/2407.11426/) (2024)
- [Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse](https://mlsanity.com/paper/2410.02273/) (2024)
- [HR-Bandit: Human-AI Collaborated Linear Recourse Bandit](https://mlsanity.com/paper/2410.14640/) (2024)
- [Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks](https://mlsanity.com/paper/2303.11111/) (2023)
- [Understanding Fixed Predictions via Confined Regions](https://mlsanity.com/paper/2502.16380/) (2025)
- [Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Users](https://mlsanity.com/paper/2503.09658/) (2025)

## Abstract (arXiv)

As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to affected individuals. Existing recourse algorithms function under the assumption that the underlying predictive model does not change. However, models are regularly updated in practice for several reasons including data distribution shifts. In this work, we make the first attempt at understanding how model updates resulting from data distribution shifts impact the algorithmic recourses generated by state-of-the-art algorithms. We carry out a rigorous theoretical and empirical analysis to address the above question. Our theoretical results establish a lower bound on the probability of recourse invalidation due to model shifts, and show the existence of a tradeoff between this invalidation probability and typical notions of "cost" minimized by modern recourse generation algorithms. We experiment with multiple synthetic and real world datasets, capturing different kinds of distribution shifts including temporal shifts, geospatial shifts, and shifts due to data correction. These experiments demonstrate that model updation due to all the aforementioned distribution shifts can potentially invalidate recourses generated by state-of-the-art algorithms. Our findings thus not only expose previously unknown flaws in the current recourse generation paradigm, but also pave the way for fundamentally rethinking the design and development of recourse generation algorithms.